Tidy Tuesday

Pretty Data Viz with ggplot and plotly

Check in beforehand:

• Can everyone practice giving a thumbs up on Zoom?
• Is this color scheme okay or should I switch to light mode?

Heads up about the way I code:

• I like displaying plots in the R Studio console instead of inline (you can switch this setting for each document using the gear)
• I’ll try to use double quotations (") instead of single (’), but in case I forget…they’re the same!
• Keyboard shortcuts: https://support.rstudio.com/hc/en-us/articles/200711853-Keyboard-Shortcuts
  • Run code
    • ctrl/cmd + enter
  • %>%
    • ctrl/cmd + shift + m
  • Insert code chunk
    • ctrl/cmd + alt + i
  • Comment out lines
    • ctrl/cmd + shift + c

# Load packages
library(tidyverse) # readr, dyplr, magrittr, ggplot2
library(plotly) # making interactive graphs
library(htmlwidgets) # Exporting graphs as HTML

# Load data
library(palmerpenguins)
penguins <- read_csv("penguins.csv")

1: IncrediBILL Palmer Penguins

Same data set as last week: Palmer Penguin dataset from LTER data.

Data exploration

# In the console
View(penguins) # Can also click on the object in the global environment
str(penguins)

## tibble [344 x 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ species          : chr [1:344] "Adelie" "Adelie" "Adelie" "Adelie" ...
##  $ island           : chr [1:344] "Torgersen" "Torgersen" "Torgersen" "Torgersen" ...
##  $ bill_length_mm   : num [1:344] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
##  $ bill_depth_mm    : num [1:344] 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
##  $ flipper_length_mm: num [1:344] 181 186 195 NA 193 190 181 195 193 190 ...
##  $ body_mass_g      : num [1:344] 3750 3800 3250 NA 3450 ...
##  $ sex              : chr [1:344] "male" "female" "female" NA ...
##  $ year             : num [1:344] 2007 2007 2007 2007 2007 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   species = col_character(),
##   ..   island = col_character(),
##   ..   bill_length_mm = col_double(),
##   ..   bill_depth_mm = col_double(),
##   ..   flipper_length_mm = col_double(),
##   ..   body_mass_g = col_double(),
##   ..   sex = col_character(),
##   ..   year = col_double()
##   .. )

tail(penguins, 5)

## # A tibble: 5 x 8
##   species island bill_length_mm bill_depth_mm flipper_length_~ body_mass_g sex  
##   <chr>   <chr>           <dbl>         <dbl>            <dbl>       <dbl> <chr>
## 1 Chinst~ Dream            55.8          19.8              207        4000 male 
## 2 Chinst~ Dream            43.5          18.1              202        3400 fema~
## 3 Chinst~ Dream            49.6          18.2              193        3775 male 
## 4 Chinst~ Dream            50.8          19                210        4100 male 
## 5 Chinst~ Dream            50.2          18.7              198        3775 fema~
## # ... with 1 more variable: year <dbl>

Static scatterplot

REVIEW: Create a scatterplot using ggplot.

• x-axis: body_mass_g
• y-axis: bill_length_mm
• point color: species

Hint: Use ggplot, geom_point, and aes(color = "")

Tips:

• ggplot2 is the package, ggplot is the function
• Things ggplot HAS to know: 1) dataset, 2) aesthetics, 3) geom
• Dataset and aes can be specified either in ggplot() or any geom_*
• Aes is for visual changes that depend on a value in the data (as opposed to visual changes that apply to all values)

ggplot(data = penguins,
       aes(x =  body_mass_g, y = bill_length_mm,
           color = species, shape = sex)) +
  geom_point(na.rm = TRUE) # suppresses warning code about removed NAs

Beautification

Tips:

• R Studio cheatsheets: https://rstudio.com/resources/cheatsheets/
• Order doesn’t matter…for the most part
• Choosing color schemes
  • Colors in R: http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf
  • Hex codes: https://coolors.co/
  • Think about data type: continuous, discrete, ordinal (ordered), diverging
• Choosing shapes
  • In R: http://www.sthda.com/english/wiki/ggplot2-point-shapes
  • Unicode: https://jrgraphix.net/r/Unicode/2600-26FF
• Adjusting themes
  • Changing elements of a theme: https://ggplot2.tidyverse.org/reference/theme.html

# Make the plot pretty

# Save a vector of colors for use in future graphs
col_scale <- c("#A3E7FC", "#32908F", "#26C485") # c() combines values into a vector
names(col_scale) <- c("Adelie", "Gentoo", "Chinstrap") # naming each value to correspond to the species

# New pretty graph
p <- penguins %>% 
  na.omit() %>% # removes ALL lines with NAs from dataset
  ggplot(aes(x = body_mass_g/1000, # can divide by 1000 to make units more manageable
             y = bill_length_mm,
             color = species, # point color depends on species
             shape = sex)) + # point shape depends on sex
  geom_point(size = 1.5, # adjust point size
             stroke = 1.1) + # adjust outline line width
  # scale_color_manual(values = "red", "yellow", "green")) + # DOESN'T WORK
  # scale_color_manual(values = c("lightskyblue", "slateblue4", "darkslategray4")) + 
  scale_color_manual(values = col_scale,
                     labels = c("P. adeliae", "P. antarcticus", "P. papua"), # order matters (unless you use a named vector!)
                     name = "Species") +
  # scale_shape_manual(values = c("\u2600", "\u2601")) + # using unicode values
  # scale_shape_manual(values = c("\u2640", "\u2642")) +
  scale_shape_manual(values = c(19, 21),
                     name = "Sex") +
  theme_minimal() + # one of the default themes
  theme(text = element_text(color = "grey80"), # ALL text color
        title = element_text(face = "bold"), # all TITLES to bold
        plot.title = element_text(color = "lightblue", # PLOT TITLE color
                                  size = 16, # text size
                                  hjust = 0.5, vjust = 5), # adjusting positioning
        axis.text = element_text(color = "grey70"), # AXIS LABELS color
        legend.text = element_text(face = "italic"), # LEGEND LABELS color
        legend.background = element_rect(fill = "grey25", # LEGEND BACKGROUND color
                                         color = NA), # no outline
        plot.margin = margin(25,15,10,10), # remember TRBL (trouble)
        plot.background = element_rect(fill = "grey20"), # entire background color
        panel.grid = element_line(color = "grey60")) + # GRID LINE color
  labs(x = "Body mass (kg)", # Make sure units match!!!
       y = "Bill length (mm)",
       title = "IncrediBILL Palmer Penguins")

p

Saving a theme

Like your theme and want to use it for another graph without copy-pasting? Save it!

• theme_set replaces the default
• theme_update adds new elements to the default theme (analagous to +)
• theme_replace replaces elements of the default theme

# What's the default?
ggplot(cars, aes(x = speed, y = dist)) + geom_point()

# Set the default as one of the included themes
theme_set(theme_classic())

ggplot(cars, aes(x = speed, y = dist)) + geom_point()

# Make your own theme!
theme_awful <- theme_dark() # save a default theme with a new name
theme_set(theme_awful) # set as the default
theme_update(panel.grid.minor = element_line(colour = "red", size = 3),
             plot.background = element_rect(fill = "yellow"),
             title = element_text(size = 60, face = "italic", color = "tan"),
             axis.text = element_text(color = "purple", face = "bold", angle = 160))

ggplot(cars, aes(x = speed, y = dist)) + geom_point()

Saving the plot

• Better to use code than the “export” button
  • Can control output size and keep values consistent
  • Whatever you do…don’t screenshot! Gives you terrible resolution.

# Check filepath
getwd() # usually run in the console

## [1] "C:/Users/kathr/Documents/git-repos/tidytuesday-06oct2020"

ggsave("penguin_plot.png", # filepath - INCLUDE FILE TYPE EXTENSION
       plot = p, # name of saved plot - default is last_plot()
       height = 4, width = 5, units = "in") # output size

Interactive scatterplot

Plotly

• Plotly is a platform that interacts with R, Python, and Julia
• Plotly focuses on creating interactive data visualizations and web applications (check out “Dash”)
• We’ll be using ggplotly, which easily turns ggplots into interactive graphs: https://plotly.com/ggplot2/

ggplotly(p) # notice that the plotly graph displays in the "Viewer" pane!

2: Flippin’ Palmer Penguins

Static scatterplot

REVIEW: Create a scatterplot using ggplot.

• x-axis: flipper_length_mm
• y-axis: body_mass_g
• point color: species, same color scheme as above (use the saved vector!)

theme_set(theme_classic()) # switching defaults since I'm not a huge fan of theme_awful...

ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g,
                     color = species)) +
  geom_point(na.rm=TRUE) + # suppress warning about NAs being removed
  scale_color_manual(values = col_scale) # same color vector as previous graph!

Quick beautification

Tips:

• Always make a static graph first - but don’t optimize the theme elements!
  • some formatting elements do not translate
  • legends, element sizes, and themes get funky with ggplotly

ggplot(penguins, aes(x = flipper_length_mm/10, y = body_mass_g/1000, # adjust units
                           fill = species)) + # changes INSIDE color of the point
  geom_point(na.rm = TRUE,
             shape = 21, # this particular shape allows for a color AND a fill
             color = "grey30") + # changes OUTSIDE color of the point
  scale_fill_manual(values = col_scale, # use scale_fill_* because we used "fill" in the aes
                    name = "Species") +
  theme_classic() + # built-in theme
  labs(x = "Flipper length (cm)", # match units!
       y = "Body mass (kg)", # match units!
       title = "Flippin' Palmer Penguins")

Interactive scatterplot

# Updated static plot
p2 <- ggplot(penguins, aes(x = flipper_length_mm/10, y = body_mass_g/1000,
                           fill = species,
                           text = paste("sex:", sex))) + # text to use for tooltip
  geom_point(na.rm = TRUE,
             size = 2, shape = 21, color = "grey30") +
  scale_fill_manual(values = col_scale,
                    name = "Species") +
  theme_classic() +
  labs(x = "Flipper length (cm)",
       y = "Body mass (kg)",
       title = "Flippin' Palmer Penguins")

# Interactive plot
ggplotly(p2,
         tooltip = c("species", "text")) # changes tooltip text

Animated scatterplot

# Updated static plot
p3 <- ggplot(penguins, aes(x = flipper_length_mm/10, y = body_mass_g/1000,
                           fill = species,
                           text = paste("sex:", sex),
                           frame = year)) + # adds animation, with one frame per year
  geom_point(na.rm = TRUE,
             size = 2, shape = 21, color = "grey30") +
  scale_fill_manual(values = col_scale,
                    name = "Species") +
  theme_classic() +
  labs(x = "Flipper length (cm)",
       y = "Body mass (kg)",
       title = "Flippin' Palmer Penguins")

# Interactive plot
p3_move <- ggplotly(p3,
                    tooltip = c("species", "text"))

p3_move

Saving the plot

Save as HTML

• Can save in the HTML output of R markdown
• Can save as a standalone HTML file using htmltools

# Save as standalone HTML
saveWidget(p3_move, "penguin_animation.html")

Export to plotly account

• Create a free plotly account: chart-studio.plotly.com
  • Find your username and API key
• Embed the plot in a webpage: https://help.plotly.com/embed-graphs-in-websites/

# Save plotly username and API key
Sys.setenv("plotly_username" = "katekathrynkat")
Sys.setenv("plotly_api_key" = "WmLiHIWtsIoNpfMz96a6")

# Explort plot to plotly
api_create(x = p3_move, # name of plot
           fileopt = "overwrite", sharing = "public")